OpenRaiser/Intern-Atlas huggingface dataset:
Recognizing and Curating Photo Albums via Event-Specific Image Importance
Venue: arXiv cs.CV 2017
Year: 2017

Automatic organization of personal photos is a problem with many real world ap- plications, and can be divided into two main tasks: recognizing the event type of the photo collection, and selecting interesting images from the collection. In this paper, we attempt to simultaneously solve both tasks: album-wise event recognition and image- wise importance prediction. We collected an album dataset with both event type labels and image importance labels, refined from an existing CUFED dataset. We propose a hybrid system consisting of three parts: A siamese network-based event-specific image importance prediction, a Convolutional Neural Network (CNN) that recognizes the event type, and a Long Short-Term Memory (LSTM)-based sequence level event recognizer. We propose an iterative updating procedure for event type and image importance score prediction. We experimentally verified that image importance score prediction and event type recognition can each help the performance of the other.
Equivalence between LINE and Matrix Factorization
Venue: arXiv cs.LG 2017
Year: 2017

LINE [1], as an efficient network embedding method, has shown its effectiveness in dealing with large-scale undirected, directed, and/or weighted networks. Particularly, it proposes to preserve both the local structure (represented by First-order Proximity) and global structure (represented by Second-order Proximity) of the network. In this study, we prove that LINE with these two proximities (LINE(1st) and LINE(2nd)) are actually factoring two different matrices separately. Specifically, LINE(1st) is factoring a matrix M (1), whose entries are the doubled Pointwise Mutual Information (PMI) of vertex pairs in undirected networks, shifted by a constant. LINE(2nd) is factoring a matrix M (2), whose entries are the PMI of vertex and context pairs in directed networks, shifted by a constant. We hope this finding would provide a basis for further extensions and generalizations of LINE.
Closed-form Solution for IMU based LSD-SLAM Point Cloud Conversion into the Scaled 3D World Environment
Venue: arXiv cs.RO 2017
Year: 2017

SLAM is a very popular research stream in computer vision and robotics nowadays. For more effective SLAM implementation it is necessary to have reliable informa- tion about the environment, also the data should be aligned and scaled according to the real world coordinate system. Monocular SLAM research is an attractive sub-stream, because of the low equipment cost, size and weight. In this paper we present a way to build a conversion from LSD-SLAM coordinate space to the real world coordinates using a true metric scale with IMU sensor data implementation. The causes of differences between the real and calculated spaces are explained and the possibility of conversions between the spaces is proved. Additionally, a closed-form solution for inter space trans- formation calculation is presented. The synthetic method of generating high level accurate and well controlled input data for the LSD-SLAM algorithm is presented. Finally, the reconstructed 3D environment representation is delivered as an output of the implemented conversion.
Detecting Parts for Action Localization
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a new framework for action localization that tracks people in videos and extracts full-body human tubes, i.e., spatio-temporal regions localizing actions, even in the case of occlusions or truncations. This is achieved by training a novel human part detector that scores visible parts while regressing full-body bounding boxes. The core of our method is a convolutional neural network which learns part proposals specific to certain body parts. These are then combined to detect people robustly in each frame. Our tracking algorithm connects the image detections temporally to extract full-body human tubes. We apply our new tube extraction method on the problem of human action localization, on the popular JHMDB dataset, and a very recent challenging dataset DALY (Daily Action Localization in YouTube), showing state-of-the-art results.
DataCite as a novel bibliometric source: Coverage, strengths and limitations
Venue: arXiv cs.DL 2017
Year: 2017

This paper explores the characteristics of DataCite to determine its possibilities and potential as a new bibliometric data source to analyze the scholarly production of open data. Open science and the increasing data sharing requirements from governments, funding bodies, institutions and scientific journals has led to a pressing demand for the development of data metrics. As a very first step towards reliable data metrics, we need to better comprehend the limitations and caveats of the information provided by sources of open data. In this paper, we critically examine records downloaded from the DataCite's OAI API and elaborate a series of recommendations regarding the use of this source for bibliometric analyses of open data. We highlight issues related to metadata incompleteness, lack of standardization, and ambiguous definitions of several fields. Despite these limitations, we emphasize DataCite's value and potential to become one of the main sources for data metrics development.
Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model
Venue: arXiv cs.CV 2017
Year: 2017

Pedestrian attribute inference is a demanding problem in visual surveillance that can facilitate person retrieval, search and indexing. To exploit semantic relations between attributes, recent research treats it as a multi-label image classification task. The visual cues hinting at attributes can be strongly localized and inference of person attributes such as hair, backpack, shorts, etc., are highly dependent on the acquired view of the pedestrian. In this paper we assert this dependence in an end-to-end learning framework and show that a view-sensitive attribute inference is able to learn better attribute predictions. Our proposed model jointly predicts the coarse pose (view) of the pedestrian and learns specialized view-specific multi-label attribute predictions. We show in an extensive evaluation on three challenging datasets (PETA, RAP and WIDER) that our proposed end-to-end view-aware attribute prediction model provides competitive performance and improves on the published state-of-the-art on these datasets.
Microblog Retrieval for Post-Disaster Relief: Applying and Comparing Neural IR Models
Venue: arXiv cs.IR 2017
Year: 2017

Microblogging sites like Twitter and Weibo have emerged as important sourcesof real-time information on ongoing events, including socio-political events, emergency events, and so on. For instance, during emergency events (such as earthquakes, floods, terror attacks), microblogging sites are very useful for gathering situational information in real-time. During such an event, typically only a small fraction of the microblogs (tweets) posted are relevant to the information need. Hence, it is necessary to design effective methodologies for microblog retrieval, so that the relevant tweets can be automatically extracted from large sets of documents (tweets). In this work, we apply and compare various neural network-based IR models for microblog retrieval for a specific application, as follows. In a disaster situation, one of the primary and practical challenges in coordinating the post-disaster relief operations is to know about what resources are needed and what resources are available in the disaster-affected area. Thus, in this study, we focus on extracting these two specific types of microblogs or tweets namely need tweets and avail tweets, which are tweets which define some needs of the people and the tweets which offer some solutions or aid for the people, respectively.
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection
Venue: arXiv cs.CL 2017
Year: 2017

The trigram `I love being' is expected to be followed by positive words such as `happy'. In a sarcastic sentence, however, the word `ignored' may be observed. The expected and the observed words are, thus, incongruous. We model sarcasm detection as the task of detecting incongruity between an observed and an expected word. In order to obtain the expected word, we use Context2Vec, a sentence completion library based on Bidirectional LSTM. However, since the exact word where such an incongruity occurs may not be known in advance, we present two approaches: an All-words approach (which consults sentence completion for every content word) and an Incongruous words-only approach (which consults sentence completion for the 50% most incongruous content words). The approaches outperform reported values for tweets but not for discussion forum posts. This is likely to be because of redundant consultation of sentence completion for discussion forum posts. Therefore, we consider an oracle case where the exact incongruous word is manually labeled in a corpus reported in past work. In this case, the performance is higher than the all-words approach. This sets up the promise for using sentence completion for sarcasm detection.
College Student Retention: When Do We Losing Them?
Venue: arXiv cs.CY 2017
Year: 2017

One of the long term goals of any college or university is increasing the student retention. The negative impact of student dropout are clear to students, parents, universities and society. The positive effect of decreasing student attrition is also self-evident including higher chance of having a better career and higher standard of life for college graduate. In view of these reasons, directors in higher education feel increasingly pressurized to outline and implement strategies to increase student retention. In this paper, we provide a detailed analysis of the student attrition problem and use statistical methods to predict when students are going to dropout from school using real case data. Our work has a number of advantages with the potential of being employed by higher education administrator of universities. We take advantage of multiple kinds of information about different aspects of student's characteristic and efficiently utilize them to make a personalized decision about the risk of dropout for a particular student.
From Bach to the Beatles: The simulation of human tonal expectation using ecologically-trained predictive models
Venue: arXiv cs.SD 2017
Year: 2017

Tonal structure is in part conveyed by statistical regularities between musical events, and research has shown that computational models reflect tonal structure in music by capturing these regularities in schematic constructs like pitch histograms. Of the few studies that model the acquisition of perceptual learning from musical data, most have employed self-organizing models that learn a topology of static descriptions of musical contexts. Also, the stimuli used to train these models are often symbolic rather than acoustically faithful representations of musical material. In this work we investigate whether sequential predictive models of musical memory (specifically, recurrent neural networks), trained on audio from commercial CD recordings, induce tonal knowledge in a similar manner to listeners (as shown in behavioral studies in music perception). Our experiments indicate that various types of recurrent neural networks produce musical expectations that clearly convey tonal structure. Furthermore, the results imply that although implicit knowledge of tonal structure is a necessary condition for accurate musical expectation, the most accurate predictive models also use other cues beyond the tonal structure of the musical context.
Modelling of Path Arrival Rate for In-Room Radio Channels with Directive Antennas
Venue: arXiv cs.IT 2017
Year: 2017

We analyze the path arrival rate for an inroom radio channel with directive antennas. The impulse response of this channel exhibits a transition from early separate components followed by a diffuse reverberation tail. Under the assumption that the transmitter's (or receiver's) position and orientation are picked uniformly at random we derive an exact expression of the mean arrival rate for a rectangular room predicted by the mirror source theory. The rate is quadratic in delay, inversely proportional to the room volume, and proportional to the product of beam coverage fractions of the transmitter and receiver antennas. Making use of the exact formula, we characterize the onset of the diffuse tail by defining a "mixing time" as the point in time where the arrival rate exceeds one component per transmit pulse duration. We also give an approximation for the power-delay spectrum. It turns out that the power-delay spectrum is unaffected by the antenna directivity. However, Monte Carlo simulations show that antenna directivity does indeed play an important role for the distribution of instantaneous mean delay and rms delay spread
Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture
Venue: arXiv cs.IR 2017
Year: 2017

We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer representations. As opposed to the neural tensor layer that has been adopted recently, the holographic composition provides the benefits of scalable and rich representational learning approach without incurring huge parameter costs. Overall, we present Holographic Dual LSTM (HD-LSTM), a unified architecture for both deep sentence modeling and semantic matching. Essentially, our model is trained end-to-end whereby the parameters of the LSTM are optimized in a way that best explains the correlation between question and answer representations. In addition, our proposed deep learning architecture requires no extensive feature engineering. Via extensive experiments, we show that HD-LSTM outperforms many other neural architectures on two popular benchmark QA datasets. Empirical studies confirm the effectiveness of holographic composition over the neural tensor layer.
Deterministic Dispersion of Mobile Robots in Dynamic Rings
Venue: arXiv cs.DC 2017
Year: 2017

In this work, we study the problem of dispersion of mobile robots on dynamic rings. The problem of dispersion of $n$ robots on an $n$ node graph, introduced by Augustine and Moses Jr. [1], requires robots to coordinate with each other and reach a configuration where exactly one robot is present on each node. This problem has real world applications and applies whenever we want to minimize the total cost of $n$ agents sharing $n$ resources, located at various places, subject to the constraint that the cost of an agent moving to a different resource is comparatively much smaller than the cost of multiple agents sharing a resource (e.g. smart electric cars sharing recharge stations). The study of this problem also provides indirect benefits to the study of scattering on graphs, the study of exploration by mobile robots, and the study of load balancing on graphs. We solve the problem of dispersion in the presence of two types of dynamism in the underlying graph: (i) vertex permutation and (ii) 1-interval connectivity. We introduce the notion of vertex permutation dynamism and have it mean that for a given set of nodes, in every round, the adversary ensures a ring structure is maintained, but the connections between the nodes may change. We use the idea of 1-interval connectivity from Di Luna et al. [10], where for a given ring, in each round, the adversary chooses at most one edge to remove. We assume robots have full visibility and present asymptotically time optimal algorithms to achieve dispersion in the presence of both types of dynamism when robots have chirality. When robots do not have chirality, we present asymptotically time optimal algorithms to achieve dispersion subject to certain constraints. Finally, we provide impossibility results for dispersion when robots have no visibility.
Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming
Venue: arXiv cs.CV 2017
Year: 2017

Reusable model design becomes desirable with the rapid expansion of computer vision and machine learning applications. In this paper, we focus on the reusability of pre-trained deep convolutional models. Specifically, different from treating pre-trained models as feature extractors, we reveal more treasures beneath convolutional layers, i.e., the convolutional activations could act as a detector for the common object in the image co-localization problem. We propose a simple yet effective method, termed Deep Descriptor Transforming (DDT), for evaluating the correlations of descriptors and then obtaining the category-consistent regions, which can accurately locate the common object in a set of unlabeled images, i.e., unsupervised object discovery. Empirical studies validate the effectiveness of the proposed DDT method. On benchmark image co-localization datasets, DDT consistently outperforms existing state-of-the-art methods by a large margin. Moreover, DDT also demonstrates good generalization ability for unseen categories and robustness for dealing with noisy data. Beyond those, DDT can be also employed for harvesting web images into valid external data sources for improving performance of both image recognition and object detection.
Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers
Venue: arXiv cs.DC 2017
Year: 2017

Performing efficient resource provisioning is a fundamental aspect for any resource provider. Local Resource Management Systems (LRMS) have been used in data centers for decades in order to obtain the best usage of the resources, providing their fair usage and partitioning for the users. In contrast, current cloud schedulers are normally based on the immediate allocation of resources on a first-come, first-served basis, meaning that a request will fail if there are no resources (e.g. OpenStack) or it will be trivially queued ordered by entry time (e.g. OpenNebula). Moreover, these scheduling strategies are based on a static partitioning of the resources, meaning that existing quotas cannot be exceeded, even if there are idle resources allocated to other projects. This is a consequence of the fact that cloud instances are not associated with a maximum execution time and leads to a situation where the resources are under-utilized. These facts have been identified by the INDIGO-DataCloud project as being too simplistic for accommodating scientific workloads in an efficient way, leading to an underutilization of the resources, a non desirable situation in scientific data centers. In this work, we will present the work done in the scheduling area during the first year of the INDIGO project and the foreseen evolutions.
A Novel Space-Time Representation on the Positive Semidefinite Con for Facial Expression Recognition
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation. We describe the temporal evolution of facial landmarks as parametrized trajectories on the Riemannian manifold of positive semidefinite matrices of fixed-rank. Our representation has the advantage to bring naturally a second desirable quantity when comparing shapes -- the spatial covariance -- in addition to the conventional affine-shape representation. We derive then geometric and computational tools for rate-invariant analysis and adaptive re-sampling of trajectories, grounding on the Riemannian geometry of the manifold. Specifically, our approach involves three steps: 1) facial landmarks are first mapped into the Riemannian manifold of positive semidefinite matrices of rank 2, to build time-parameterized trajectories; 2) a temporal alignment is performed on the trajectories, providing a geometry-aware (dis-)similarity measure between them; 3) finally, pairwise proximity function SVM (ppfSVM) is used to classify them, incorporating the latter (dis-)similarity measure into the kernel function. We show the effectiveness of the proposed approach on four publicly available benchmarks (CK+, MMI, Oulu-CASIA, and AFEW). The results of the proposed approach are comparable to or better than the state-of-the-art methods when involving only facial landmarks.
An ASM-based Characterization of Starvation-free Systems
Venue: arXiv cs.DC 2017
Year: 2017

Abstract State Machines (ASMs) have been successfully applied for modeling critical and complex systems in a wide range of application domains. However, unlike other well-known formalisms, e.g. Petri nets, ASMs lack inherent, domain-independent characterisations of computationally important properties. Here, we provide an ASM-based characterisation of the starvation-free property. The classic, informal notion of starvation, usually provided in literature, is analysed and expressed as a necessary condition in terms of ASMs. Thus, we enrich the ASM framework with the notion of vulnerable rule as a practical tool for analysing starvation issues in an operational fashion
RAIL: Risk-Averse Imitation Learning
Venue: arXiv cs.LG 2017
Year: 2017

Imitation learning algorithms learn viable policies by imitating an expert's behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies when the expert's behavior is available as a fixed set of trajectories. We evaluate in terms of the expert's cost function and observe that the distribution of trajectory-costs is often more heavy-tailed for GAIL-agents than the expert at a number of benchmark continuous-control tasks. Thus, high-cost trajectories, corresponding to tail-end events of catastrophic failure, are more likely to be encountered by the GAIL-agents than the expert. This makes the reliability of GAIL-agents questionable when it comes to deployment in risk-sensitive applications like robotic surgery and autonomous driving. In this work, we aim to minimize the occurrence of tail-end events by minimizing tail risk within the GAIL framework. We quantify tail risk by the Conditional-Value-at-Risk (CVaR) of trajectories and develop the Risk-Averse Imitation Learning (RAIL) algorithm. We observe that the policies learned with RAIL show lower tail-end risk than those of vanilla GAIL. Thus the proposed RAIL algorithm appears as a potent alternative to GAIL for improved reliability in risk-sensitive applications.
Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology
Venue: arXiv cs.GR 2017
Year: 2017

Topological data analysis is an emerging area in exploratory data analysis and data mining. Its main tool, persistent homology, has become a popular technique to study the structure of complex, high-dimensional data. In this paper, we propose a novel method using persistent homology to quantify structural changes in time-varying graphs. Specifically, we transform each instance of the time-varying graph into metric spaces, extract topological features using persistent homology, and compare those features over time. We provide a visualization that assists in time-varying graph exploration and helps to identify patterns of behavior within the data. To validate our approach, we conduct several case studies on real world data sets and show how our method can find cyclic patterns, deviations from those patterns, and one-time events in time-varying graphs. We also examine whether persistence-based similarity measure as a graph metric satisfies a set of well-established, desirable properties for graph metrics.